On the sequential track correlation algorithm in a multisensor data fusion system

Shalom, Yaar

doi:10.1109/taes.2008.4517016

Cited by 23 publications

(7 citation statements)

References 5 publications

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“…One of them is track correlation mass and the other one is track separation mass. Similar to the association mass (La Scala and Farina, 2002;Bar-Shalom, 2008), the track correlation mass m ii (l) denotes the times of track i from node 1 correlated with track j from node 2 till time l and the separation mass of track i and j is defined as follow Equation (19):…”

Section: Track Mass Designingmentioning

confidence: 99%

“…Track-to-trackassociatio n problem (You et al, 1996;Singer and Kanyuck, 1971;Bar-Shalom and Fortmann, 1988;Gul, 1994;Kosoka, 1983;You et al, 1989;Bowman, 1979;Chang and Youens, 1982;Bar-Shalom and Chen, 2004;Kaplan et al, 2008;Tian and BarShalom, 2011;Bar-Shalom and Campo, 1986;Mori et al, 2011;Osbome et al, 2011;Wang et al, 2012;La Scala and Farina, 2002; Bar-Shalom, 2008) is a crux of distributed multisensor system. It's a problem of how to decide whether two tracks coming from different sensor systems represent the same target.…”

Section: Introductionmentioning

confidence: 99%

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New Track-to-Track Correlation Algorithms Based on Bithreshold in a Distributed Multisensor Information Fusion System

A¹

2013

Journal of Computer Science

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Track-to-Track correlation (or association) is an ongoing area of interest in the field of distributed multisensory information fusion. In order to perform accurately identifying tracks with common origin and get fast convergence, this study presents independent and dependent Bi-threshold Track Correlation Algorithms (called BTCAs), which are described in detail and the track correlation mass and multivalency processing methods are discussed as well. Then, Based on BTCAs, two modified Bithreshold Track Correlation Algorithms with average Test Statistic (called BTCA-TSs) are proposed. Finally, simulations are designed to compare the correlation performance of these algorithms with that of Singer's and Bar-Shalom's algorithms. The simulation results show that the performance of these algorithms proposed in this study is much better than that of the classical methods under the environments of dense targets, interfering, noise and track cross and so on.

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Section: Track Mass Designingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

New Track-to-Track Correlation Algorithms Based on Bithreshold in a Distributed Multisensor Information Fusion System

A¹

2013

Journal of Computer Science

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“…In traditional MF, data association is tipically performed at first to provide measurement-to-object correspondence, then a bank of independent Kalman filters, one for each object, are used to estimate the object states. Track-to-track fusion (T2TF) [15] is adopted to associate object tracks of different agents so that the asoociated tracks can then be combined according to either optimal fusion [16], [17], [18], if the cross-correlations among different agents are known, or otherwise covariance intersection [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Multiobject Fusion With Minimum Information Loss

Gao

Battistelli

Chisci

2020

IEEE Signal Process. Lett.

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Generalized covariance intersection (GCI) has been effective in fusing multiobject densities from multiple agents for multitarget tracking and mapping purposes. From an information-theoretic viewpoint, it has been shown that GCI fusion essentially minimizes the weighted information gain (WIG) from local densities to the fused one. In this paper, the interest is in the fusion rule that dually minimizes the information loss (IL) and it turns out that such a fusion rule is consistent with the so-called linear opinion pool (LOP). However, the LOP cannot be directly applied to multiobject fusion since the resulting fused multiobject density (FMD), in general, no longer belongs to the same family of the local ones, thus it cannot be utilized as prior information for the next recursion in the context of Bayesian multiobject filtering. In order to overcome such a difficulty, the principle of minimizing IL is further exploited in that the optimal FMD in the same family of the local ones is looked for. Implementation issues relative to the proposed minimum IL (MWIL) fusion rule are discussed. Finally, the performance of the MWIL rule is assessed via simulation experiments concerning distributed multitarget tracking over a wireless sensor network.

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“…Many algorithms have been proposed to solve the track-to-track association problem, such as Singer's algorithm [6,7], Bar-Shalom's algorithm [8,9], Sequential Algorithm [10,11], assignment algorithm [12], likelihood ratio test algorithm [13], multiple hypothesis test algorithm [14], and so on. The current track-to-track association algorithms have good performance in the ideal simulation environments, but when using in practical application it is often encountered that the probability of track correct correlation seriously falls and the probability of track false correlation and missing correlation greatly rise.…”

Section: Introductionmentioning

confidence: 99%

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

Yang¹,

Song²,

Qu³

et al. 2013

JSIP

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The presence of systematic measuring errors complicates track-to-track association, spatially separates the tracks that correspond to the same true target, and seriously decline the performances of traditional track-to-track association algorithms. Consequently, the influence of radar systematic errors on tracks from different radars, which is described as some rotation and translation, has been analyzed theoretically in this paper. In addition, a novel approach named alignment-correlation method is developed to estimate and reduce this effect, align and correlate tracks accurately without prior registration using phase correlation technique and statistic binary track correlation algorithm. Monte-Carlo simulation results illustrate that the proposed algorithm has good performance in solving the track-to-track association problem with systematic errors in radar network and could provide effective and reliable associated tracks for the next step of registration.

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On the sequential track correlation algorithm in a multisensor data fusion system

Abstract: This note points out that the recently published sequential track correlation algorithm overlooked the correlation in time of the state estimation errors.

Cited by 23 publications

References 5 publications

New Track-to-Track Correlation Algorithms Based on Bithreshold in a Distributed Multisensor Information Fusion System

New Track-to-Track Correlation Algorithms Based on Bithreshold in a Distributed Multisensor Information Fusion System

Multiobject Fusion With Minimum Information Loss

Track-to-Track Association Technique in Radar Network in the Presence of Systematic Errors

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